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Uncoop.xyz is a website that is currently up for sale. The domain owner is offering it for an asking price of 249 USD. The webpage was generated using Sedo Domain Parking. Please note that Sedo, the platform used for domain parking, has no direct relationship with third-party advertisers. Any reference to a specific service or trademark on the website is not controlled by Sedo and does not imply any association, endorsement, or recommendation by Sedo. For more information, you can refer to the Privacy Policy.

The website page seems to be a placeholder or error page with the message 'Site Not Found'. It indicates that the user may not have deployed an app yet or may have an empty directory. The page suggests referring to hosting documentation to deploy the first app. The site appears to be under construction or experiencing technical issues.

The website displays a '404: NOT_FOUND' error message along with a code and ID indicating a deployment not found issue. Users encountering this error are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::zdhct-1723140771934-b5e5ad909fad'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::tszrz-1723627812794-26f3e29ebbda). Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::t6mdp-1736442717535-3a5d4eeaf597'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are directed to refer to the documentation for more information and troubleshooting.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::bkk2q-1735577647183-b74af8a896dd'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::xsfmp-1735923123827-c52a4ded5ea0) for reference. Users are directed to check the documentation for more information and troubleshooting tips.

The website is currently displaying a 503 error message indicating that the deployment is paused by the owner. It suggests that the visitor should contact the website owner or try again later. The error code provided is DEPLOYMENT_PAUSED with an ID of sin1::pz26b-1735577555137-5602c3cb561a. It advises the owner to refer to the documentation section or resume service.

LLMChess is a web-based chess game that utilizes large language models (LLMs) to power the gameplay. Players can select the LLM model they wish to play against, and the game will commence once the "Start" button is clicked. The game logs are displayed in a black-bordered pane on the right-hand side of the screen. LLMChess is compatible with the Google Chrome browser. For more information on the game's functionality and participation guidelines, please refer to the provided link.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. The error code is 'DEPLOYMENT_NOT_FOUND' with an ID of 'sin1::llkll-1726766020379-aede70059d09'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::2l69n-1735059638029-7301ded2d892). Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message along with a code and ID indicating a deployment not found. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are directed to refer to the documentation for more information and troubleshooting assistance.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::mk7hv-1736442739549-625ea5452a6a). The message advises users to refer to the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::z2jxr-1736614143090-03728368920f'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are advised to refer to the documentation for more information and troubleshooting.

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imbazix.co is a website that is currently for sale. The webpage seems to have been generated by the domain owner using Sedo Domain Parking. The site provides resources and information related to the domain name 'imbazix.co'. It is important to note that Sedo, the platform used for domain parking, does not have a direct relationship with third-party advertisers. The website does not offer any specific services or products but serves as a placeholder for the domain. Visitors should refer to the Privacy Policy for more information.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

MicroLens is a content-driven micro-video recommendation dataset at scale. It provides a large dataset with multimodal data, including raw text, images, audio, video, and video comments, for tasks such as multi-modal recommendation, foundation model building, and fairness recommendation. The dataset is available in two versions: MicroLens-50K and MicroLens-100K, with extracted features for multimodal recommendation tasks. Researchers can access the dataset through provided links and reach out to the corresponding author for the complete dataset. The repository also includes codes for various algorithms like VideoRec, IDRec, and VIDRec, each implementing different video models and baselines.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

Lobe Chat is an open-source, modern-design ChatGPT/LLMs UI/Framework. Supports speech-synthesis, multi-modal, and extensible ([function call][docs-functionc-call]) plugin system. One-click **FREE** deployment of your private OpenAI ChatGPT/Claude/Gemini/Groq/Ollama chat application.

AI Hospital is a research repository focusing on the interactive evaluation and collaboration of Large Language Models (LLMs) as intern doctors for clinical diagnosis. The repository includes a simulation module tailored for various medical roles, introduces the Multi-View Medical Evaluation (MVME) Benchmark, provides dialog history documents of LLMs, replication instructions, performance evaluation, and guidance for creating intern doctor agents. The collaborative diagnosis with LLMs emphasizes dispute resolution. The study was authored by Zhihao Fan, Jialong Tang, Wei Chen, Siyuan Wang, Zhongyu Wei, Jun Xie, Fei Huang, and Jingren Zhou.

This repository contains a C++ application that demonstrates face recognition capabilities using computer vision techniques. The demo utilizes OpenCV and dlib libraries for efficient face detection and recognition with 3D passive face liveness detection (face anti-spoofing). Key Features: Face detection: The SDK utilizes advanced computer vision techniques to detect faces in images or video frames, enabling a wide range of applications. Face recognition: It can recognize known faces by comparing them with a pre-defined database of individuals. Age estimation: It can estimate the age of detected faces. Gender detection: It can determine the gender of detected faces. Liveness detection: It can detect whether a face is from a live person or a static image.

Julep is an advanced platform for creating stateful and functional AI apps powered by large language models. It offers features like statefulness by design, automatic function calling, production-ready deployment, cron-like asynchronous functions, 90+ built-in tools, and the ability to switch between different LLMs easily. Users can build AI applications without the need to write code for embedding, saving, and retrieving conversation history, and can connect to third-party applications using Composio. Julep simplifies the process of getting started with AI apps, whether they are conversational, functional, or agentic.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

Tokencost is a clientside tool for calculating the USD cost of using major Large Language Model (LLMs) APIs by estimating the cost of prompts and completions. It helps track the latest price changes of major LLM providers, accurately count prompt tokens before sending OpenAI requests, and easily integrate to get the cost of a prompt or completion with a single function. Users can calculate prompt and completion costs using OpenAI requests, count tokens in prompts formatted as message lists or string prompts, and refer to a cost table with updated prices for various LLM models. The tool also supports callback handlers for LLM wrapper/framework libraries like LlamaIndex and Langchain.

screeps-starter-rust is a Rust AI starter kit for Screeps: World, a JavaScript-based MMO game. It utilizes the screeps-game-api bindings from the rustyscreeps organization and wasm-pack for building Rust code to WebAssembly. The example includes Rollup for bundling javascript, Babel for transpiling code, and screeps-api Node.js package for deployment. Users can refer to the Rust version of game APIs documentation at https://docs.rs/screeps-game-api/. The tool supports most crates on crates.io, except those interacting with OS APIs.

This repository provides guidance on building a multi-tenant SaaS solution for accessing foundation models using Amazon Bedrock and Amazon SageMaker. It helps enterprise IT teams track usage and costs of foundation models, regulate access, and provide visibility to cost centers. The solution includes an API Gateway design pattern for standardization and governance, enabling loose coupling between model consumers and endpoint services. The CDK Stack deploys resources for private networking, API Gateway, Lambda functions, DynamoDB table, EventBridge, S3 buckets, and Cloudwatch logs.

This is the official Clarifai gRPC Python client for interacting with their recognition API. Clarifai offers a platform for data scientists, developers, researchers, and enterprises to utilize artificial intelligence for image, video, and text analysis through computer vision and natural language processing. The client allows users to authenticate, predict concepts in images, and access various functionalities provided by the Clarifai API. It follows a versioning scheme that aligns with the backend API updates and includes specific instructions for installation and troubleshooting. Users can explore the Clarifai demo, sign up for an account, and refer to the documentation for detailed information.

qlora-pipe is a pipeline parallel training script designed for efficiently training large language models that cannot fit on one GPU. It supports QLoRA, LoRA, and full fine-tuning, with efficient model loading and the ability to load any dataset that Axolotl can handle. The script allows for raw text training, resuming training from a checkpoint, logging metrics to Tensorboard, specifying a separate evaluation dataset, training on multiple datasets simultaneously, and supports various models like Llama, Mistral, Mixtral, Qwen-1.5, and Cohere (Command R). It handles pipeline- and data-parallelism using Deepspeed, enabling users to set the number of GPUs, pipeline stages, and gradient accumulation steps for optimal utilization.

Pax is a framework to configure and run machine learning experiments on top of Jax.

Asistente médico amable y metódico para el triaje individual

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"Chip" refers to the chip on this bot's shoulder. he's...not friendly. But he's still helpful, even when he's insulting you.

Diagnose und Bereitstellung von Behandlungsmethoden - deutsche Sprache

Olá radiologista. Para começar, digite /prelim e escreva o tipo de exame e os seus achados preliminares, logo em seguida enviarei o laudo completo!